CBTI Summary

Consort map

Demographic information

Characteristic

N

Overall, N = 3581

control, N = 1791

treatment, N = 1791

p-value2

age

358

36.34 ± 13.94 (18 - 73)

35.95 ± 13.84 (18 - 73)

36.72 ± 14.07 (18 - 71)

0.599

gender

358

0.792

female

286 (80%)

142 (79%)

144 (80%)

male

72 (20%)

37 (21%)

35 (20%)

occupation

358

0.658

civil

13 (3.6%)

4 (2.2%)

9 (5.0%)

clerk

57 (16%)

30 (17%)

27 (15%)

craft

12 (3.4%)

8 (4.5%)

4 (2.2%)

homemaker

26 (7.3%)

14 (7.8%)

12 (6.7%)

manager

28 (7.8%)

16 (8.9%)

12 (6.7%)

other

15 (4.2%)

5 (2.8%)

10 (5.6%)

professional

39 (11%)

16 (8.9%)

23 (13%)

retired

21 (5.9%)

10 (5.6%)

11 (6.1%)

service

12 (3.4%)

7 (3.9%)

5 (2.8%)

student

119 (33%)

60 (34%)

59 (33%)

unemploy

16 (4.5%)

9 (5.0%)

7 (3.9%)

marital

358

0.652

divorced

14 (3.9%)

5 (2.8%)

9 (5.0%)

married

97 (27%)

51 (28%)

46 (26%)

other

2 (0.6%)

1 (0.6%)

1 (0.6%)

separated

5 (1.4%)

1 (0.6%)

4 (2.2%)

single

235 (66%)

119 (66%)

116 (65%)

widowed

5 (1.4%)

2 (1.1%)

3 (1.7%)

marital_r

358

0.252

married

97 (27%)

51 (28%)

46 (26%)

other

26 (7.3%)

9 (5.0%)

17 (9.5%)

single

235 (66%)

119 (66%)

116 (65%)

education

358

0.914

post-secondary

52 (15%)

28 (16%)

24 (13%)

primary

2 (0.6%)

1 (0.6%)

1 (0.6%)

secondary

50 (14%)

24 (13%)

26 (15%)

university

254 (71%)

126 (70%)

128 (72%)

education_r

358

0.819

post-secondary

52 (15%)

28 (16%)

24 (13%)

secondary or below

52 (15%)

25 (14%)

27 (15%)

university

254 (71%)

126 (70%)

128 (72%)

family_income

358

0.502

0_10000

56 (16%)

27 (15%)

29 (16%)

10001_20000

75 (21%)

38 (21%)

37 (21%)

20001_30000

73 (20%)

42 (23%)

31 (17%)

30001_40000

60 (17%)

31 (17%)

29 (16%)

40000_above

94 (26%)

41 (23%)

53 (30%)

religion

358

0.110

buddhism

16 (4.5%)

7 (3.9%)

9 (5.0%)

catholic

17 (4.7%)

11 (6.1%)

6 (3.4%)

christianity

73 (20%)

30 (17%)

43 (24%)

nil

248 (69%)

130 (73%)

118 (66%)

other

3 (0.8%)

0 (0%)

3 (1.7%)

taoism

1 (0.3%)

1 (0.6%)

0 (0%)

religion_r

358

0.234

buddhism

16 (4.5%)

7 (3.9%)

9 (5.0%)

catholic

17 (4.7%)

11 (6.1%)

6 (3.4%)

christianity

73 (20%)

30 (17%)

43 (24%)

nil

248 (69%)

130 (73%)

118 (66%)

other

4 (1.1%)

1 (0.6%)

3 (1.7%)

source

358

0.233

bokss

15 (4.2%)

11 (6.1%)

4 (2.2%)

facebook

131 (37%)

63 (35%)

68 (38%)

instagram

12 (3.4%)

7 (3.9%)

5 (2.8%)

other

66 (18%)

28 (16%)

38 (21%)

refresh

134 (37%)

70 (39%)

64 (36%)

1Mean ± SD (Range); n (%)

2Two Sample t-test; Pearson's Chi-squared test; Fisher's exact test

Measurement

Table

Characteristic

N

Overall, N = 3581

control, N = 1791

treatment, N = 1791

p-value2

isi

358

13.47 ± 3.37 (8 - 21)

13.53 ± 3.33 (8 - 21)

13.40 ± 3.42 (8 - 21)

0.719

who

358

9.90 ± 3.74 (0 - 21)

9.82 ± 3.71 (1 - 20)

9.98 ± 3.77 (0 - 21)

0.682

phq

358

8.51 ± 5.01 (0 - 25)

8.21 ± 4.98 (0 - 21)

8.80 ± 5.03 (0 - 25)

0.264

gad

358

7.78 ± 5.12 (0 - 21)

7.54 ± 5.03 (0 - 21)

8.02 ± 5.21 (0 - 21)

0.376

wsas

358

16.73 ± 9.85 (0 - 40)

16.77 ± 9.70 (0 - 39)

16.69 ± 10.03 (0 - 40)

0.936

shps_arousal

358

3.10 ± 0.69 (1 - 5)

3.02 ± 0.68 (1 - 5)

3.18 ± 0.69 (1 - 5)

0.025

shps_schedule

358

3.55 ± 0.87 (1 - 6)

3.53 ± 0.81 (2 - 6)

3.58 ± 0.93 (1 - 6)

0.653

shps_behavior

358

2.05 ± 0.66 (1 - 4)

1.99 ± 0.61 (1 - 4)

2.12 ± 0.71 (1 - 4)

0.059

shps_environment

358

2.30 ± 0.82 (1 - 5)

2.33 ± 0.84 (1 - 5)

2.27 ± 0.80 (1 - 5)

0.473

dbas_consequence

358

6.61 ± 1.75 (1 - 10)

6.59 ± 1.82 (1 - 10)

6.64 ± 1.68 (1 - 10)

0.772

dbas_worry

358

14.37 ± 3.23 (3 - 20)

14.20 ± 3.35 (3 - 20)

14.54 ± 3.11 (3 - 20)

0.319

dbas_expectation

358

7.03 ± 2.14 (1 - 10)

7.17 ± 2.09 (1 - 10)

6.89 ± 2.19 (1 - 10)

0.209

dbas_medication

358

3.19 ± 2.07 (0 - 9)

3.15 ± 2.04 (0 - 9)

3.24 ± 2.09 (0 - 9)

0.683

psas_somatic

358

1.88 ± 0.69 (1 - 5)

1.86 ± 0.66 (1 - 4)

1.91 ± 0.71 (1 - 5)

0.539

psas_cognitive

358

2.92 ± 0.85 (1 - 5)

2.87 ± 0.84 (1 - 5)

2.97 ± 0.86 (1 - 5)

0.270

psqi_global

358

10.87 ± 3.02 (2 - 19)

10.72 ± 3.03 (4 - 17)

11.01 ± 3.00 (2 - 19)

0.363

mic_attention

358

1.36 ± 0.72 (0 - 3)

1.30 ± 0.71 (0 - 3)

1.42 ± 0.73 (0 - 3)

0.110

mic_executive

358

1.31 ± 0.76 (0 - 3)

1.28 ± 0.77 (0 - 3)

1.35 ± 0.76 (0 - 3)

0.406

mic_memory

358

1.37 ± 0.73 (0 - 3)

1.33 ± 0.75 (0 - 3)

1.40 ± 0.71 (0 - 3)

0.397

nb_pcs

358

46.27 ± 8.63 (17 - 65)

46.33 ± 8.91 (17 - 63)

46.20 ± 8.38 (21 - 65)

0.879

nb_mcs

358

39.94 ± 9.95 (8 - 65)

39.90 ± 9.78 (8 - 62)

39.98 ± 10.14 (8 - 65)

0.935

1Mean ± SD (Range)

2Two Sample t-test

Plot

Data analysis

Table

Group

Characteristic

Beta

SE1

95% CI1

p-value

isi

(Intercept)

13.5

0.286

13.0, 14.1

group

control

—

—

—

treatment

-0.128

0.404

-0.921, 0.664

0.751

time_point

1st

—

—

—

2nd

-2.46

0.322

-3.09, -1.83

0.000

3rd

-2.87

0.330

-3.52, -2.22

0.000

group * time_point

treatment * 2nd

-2.96

0.486

-3.92, -2.01

0.000

treatment * 3rd

-2.96

0.495

-3.93, -1.99

0.000

Pseudo R square

0.260

who

(Intercept)

9.82

0.305

9.22, 10.4

group

control

—

—

—

treatment

0.162

0.432

-0.684, 1.01

0.708

time_point

1st

—

—

—

2nd

0.729

0.299

0.144, 1.31

0.015

3rd

0.930

0.306

0.329, 1.53

0.003

group * time_point

treatment * 2nd

1.40

0.452

0.515, 2.29

0.002

treatment * 3rd

1.63

0.461

0.725, 2.53

0.000

Pseudo R square

0.053

phq

(Intercept)

8.21

0.378

7.47, 8.95

group

control

—

—

—

treatment

0.592

0.535

-0.457, 1.64

0.269

time_point

1st

—

—

—

2nd

-0.779

0.333

-1.43, -0.126

0.020

3rd

-0.665

0.342

-1.33, 0.005

0.052

group * time_point

treatment * 2nd

-1.73

0.506

-2.73, -0.742

0.001

treatment * 3rd

-2.42

0.516

-3.43, -1.41

0.000

Pseudo R square

0.039

gad

(Intercept)

7.54

0.382

6.79, 8.28

group

control

—

—

—

treatment

0.480

0.540

-0.577, 1.54

0.374

time_point

1st

—

—

—

2nd

-0.440

0.341

-1.11, 0.228

0.197

3rd

-0.580

0.349

-1.27, 0.105

0.097

group * time_point

treatment * 2nd

-2.07

0.517

-3.09, -1.06

0.000

treatment * 3rd

-2.38

0.527

-3.42, -1.35

0.000

Pseudo R square

0.038

wsas

(Intercept)

16.8

0.748

15.3, 18.2

group

control

—

—

—

treatment

-0.084

1.058

-2.16, 1.99

0.937

time_point

1st

—

—

—

2nd

-0.819

0.694

-2.18, 0.542

0.239

3rd

-0.132

0.712

-1.53, 1.26

0.853

group * time_point

treatment * 2nd

-2.95

1.053

-5.02, -0.888

0.005

treatment * 3rd

-4.90

1.074

-7.01, -2.80

0.000

Pseudo R square

0.034

shps_arousal

(Intercept)

3.02

0.055

2.91, 3.13

group

control

—

—

—

treatment

0.163

0.078

0.009, 0.316

0.039

time_point

1st

—

—

—

2nd

-0.196

0.059

-0.312, -0.080

0.001

3rd

-0.221

0.061

-0.340, -0.102

0.000

group * time_point

treatment * 2nd

-0.477

0.089

-0.653, -0.302

0.000

treatment * 3rd

-0.563

0.091

-0.741, -0.384

0.000

Pseudo R square

0.112

shps_schedule

(Intercept)

3.53

0.067

3.40, 3.66

group

control

—

—

—

treatment

0.042

0.094

-0.143, 0.226

0.659

time_point

1st

—

—

—

2nd

-0.101

0.060

-0.218, 0.017

0.094

3rd

-0.136

0.061

-0.256, -0.015

0.028

group * time_point

treatment * 2nd

-0.345

0.091

-0.523, -0.167

0.000

treatment * 3rd

-0.422

0.093

-0.603, -0.240

0.000

Pseudo R square

0.045

shps_behavior

(Intercept)

1.99

0.051

1.89, 2.08

group

control

—

—

—

treatment

0.132

0.072

-0.009, 0.273

0.067

time_point

1st

—

—

—

2nd

0.024

0.051

-0.075, 0.124

0.629

3rd

0.009

0.052

-0.092, 0.111

0.858

group * time_point

treatment * 2nd

-0.244

0.077

-0.394, -0.094

0.002

treatment * 3rd

-0.333

0.078

-0.486, -0.180

0.000

Pseudo R square

0.019

shps_environment

(Intercept)

2.33

0.061

2.21, 2.45

group

control

—

—

—

treatment

-0.062

0.086

-0.230, 0.106

0.469

time_point

1st

—

—

—

2nd

-0.058

0.060

-0.176, 0.059

0.331

3rd

-0.059

0.061

-0.180, 0.061

0.333

group * time_point

treatment * 2nd

-0.085

0.091

-0.263, 0.092

0.346

treatment * 3rd

-0.259

0.092

-0.440, -0.078

0.005

Pseudo R square

0.021

dbas_consequence

(Intercept)

6.59

0.140

6.31, 6.86

group

control

—

—

—

treatment

0.054

0.199

-0.336, 0.443

0.787

time_point

1st

—

—

—

2nd

-0.336

0.141

-0.612, -0.061

0.017

3rd

-0.670

0.144

-0.953, -0.387

0.000

group * time_point

treatment * 2nd

-1.11

0.213

-1.53, -0.693

0.000

treatment * 3rd

-1.30

0.217

-1.72, -0.873

0.000

Pseudo R square

0.117

dbas_worry

(Intercept)

14.2

0.284

13.6, 14.8

group

control

—

—

—

treatment

0.341

0.401

-0.445, 1.13

0.396

time_point

1st

—

—

—

2nd

-1.23

0.323

-1.86, -0.599

0.000

3rd

-1.83

0.331

-2.48, -1.18

0.000

group * time_point

treatment * 2nd

-2.71

0.486

-3.67, -1.76

0.000

treatment * 3rd

-2.88

0.495

-3.85, -1.91

0.000

Pseudo R square

0.162

dbas_expectation

(Intercept)

7.17

0.172

6.84, 7.51

group

control

—

—

—

treatment

-0.285

0.244

-0.763, 0.193

0.243

time_point

1st

—

—

—

2nd

-0.343

0.176

-0.687, 0.001

0.051

3rd

-0.777

0.180

-1.13, -0.424

0.000

group * time_point

treatment * 2nd

-1.25

0.266

-1.77, -0.727

0.000

treatment * 3rd

-1.28

0.271

-1.81, -0.748

0.000

Pseudo R square

0.111

dbas_medication

(Intercept)

3.15

0.161

2.83, 3.46

group

control

—

—

—

treatment

0.089

0.228

-0.357, 0.536

0.695

time_point

1st

—

—

—

2nd

0.366

0.164

0.045, 0.688

0.026

3rd

0.308

0.168

-0.022, 0.638

0.068

group * time_point

treatment * 2nd

-0.664

0.248

-1.15, -0.177

0.008

treatment * 3rd

-0.859

0.253

-1.35, -0.363

0.001

Pseudo R square

0.015

psas_somatic

(Intercept)

1.86

0.051

1.76, 1.96

group

control

—

—

—

treatment

0.045

0.072

-0.096, 0.185

0.533

time_point

1st

—

—

—

2nd

0.143

0.047

0.051, 0.236

0.003

3rd

0.008

0.049

-0.087, 0.103

0.867

group * time_point

treatment * 2nd

-0.306

0.072

-0.447, -0.166

0.000

treatment * 3rd

-0.240

0.073

-0.384, -0.097

0.001

Pseudo R square

0.021

psas_cognitive

(Intercept)

2.87

0.063

2.75, 3.00

group

control

—

—

—

treatment

0.099

0.090

-0.077, 0.275

0.269

time_point

1st

—

—

—

2nd

-0.204

0.064

-0.329, -0.079

0.001

3rd

-0.356

0.065

-0.484, -0.227

0.000

group * time_point

treatment * 2nd

-0.434

0.097

-0.623, -0.245

0.000

treatment * 3rd

-0.414

0.098

-0.607, -0.221

0.000

Pseudo R square

0.091

psqi_global

(Intercept)

10.7

0.237

10.3, 11.2

group

control

—

—

—

treatment

0.291

0.335

-0.366, 0.947

0.386

time_point

1st

—

—

—

2nd

-1.31

0.258

-1.82, -0.808

0.000

3rd

-1.32

0.264

-1.84, -0.801

0.000

group * time_point

treatment * 2nd

-1.86

0.389

-2.62, -1.10

0.000

treatment * 3rd

-2.44

0.396

-3.22, -1.66

0.000

Pseudo R square

0.149

mic_attention

(Intercept)

1.30

0.057

1.19, 1.41

group

control

—

—

—

treatment

0.122

0.080

-0.035, 0.278

0.130

time_point

1st

—

—

—

2nd

-0.022

0.055

-0.130, 0.087

0.693

3rd

0.029

0.057

-0.083, 0.140

0.614

group * time_point

treatment * 2nd

-0.248

0.084

-0.412, -0.083

0.003

treatment * 3rd

-0.382

0.086

-0.550, -0.215

0.000

Pseudo R square

0.021

mic_executive

(Intercept)

1.28

0.058

1.17, 1.39

group

control

—

—

—

treatment

0.067

0.082

-0.094, 0.228

0.415

time_point

1st

—

—

—

2nd

-0.034

0.054

-0.140, 0.073

0.537

3rd

-0.048

0.056

-0.157, 0.062

0.394

group * time_point

treatment * 2nd

-0.159

0.082

-0.321, 0.002

0.054

treatment * 3rd

-0.273

0.084

-0.438, -0.108

0.001

Pseudo R square

0.015

mic_memory

(Intercept)

1.33

0.057

1.22, 1.44

group

control

—

—

—

treatment

0.066

0.081

-0.093, 0.224

0.417

time_point

1st

—

—

—

2nd

0.031

0.051

-0.069, 0.132

0.537

3rd

-0.060

0.052

-0.162, 0.043

0.254

group * time_point

treatment * 2nd

-0.276

0.078

-0.428, -0.124

0.000

treatment * 3rd

-0.223

0.079

-0.378, -0.068

0.005

Pseudo R square

0.017

nb_pcs

(Intercept)

46.3

0.659

45.0, 47.6

group

control

—

—

—

treatment

-0.139

0.931

-1.96, 1.69

0.882

time_point

1st

—

—

—

2nd

-0.871

0.590

-2.03, 0.284

0.140

3rd

-0.753

0.605

-1.94, 0.432

0.214

group * time_point

treatment * 2nd

2.76

0.895

1.00, 4.51

0.002

treatment * 3rd

3.17

0.913

1.38, 4.96

0.001

Pseudo R square

0.015

nb_mcs

(Intercept)

39.9

0.771

38.4, 41.4

group

control

—

—

—

treatment

0.085

1.090

-2.05, 2.22

0.938

time_point

1st

—

—

—

2nd

2.00

0.739

0.555, 3.45

0.007

3rd

2.30

0.758

0.812, 3.78

0.003

group * time_point

treatment * 2nd

3.57

1.120

1.37, 5.77

0.002

treatment * 3rd

4.63

1.141

2.40, 6.87

0.000

Pseudo R square

0.056

1SE = Standard Error, CI = Confidence Interval

Text

isi

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict isi with group and time_point (formula: isi ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.59) and the part related to the fixed effects alone (marginal R2) is of 0.26. The model’s intercept, corresponding to group = control and time_point = 1st, is at 13.53 (95% CI [12.97, 14.09], t(850) = 47.32, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.13, 95% CI [-0.92, 0.66], t(850) = -0.32, p = 0.751; Std. beta = -0.03, 95% CI [-0.21, 0.15])
  • The effect of time point [2nd] is statistically significant and negative (beta = -2.46, 95% CI [-3.09, -1.83], t(850) = -7.62, p < .001; Std. beta = -0.55, 95% CI [-0.69, -0.41])
  • The effect of time point [3rd] is statistically significant and negative (beta = -2.87, 95% CI [-3.52, -2.22], t(850) = -8.69, p < .001; Std. beta = -0.64, 95% CI [-0.78, -0.50])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -2.96, 95% CI [-3.92, -2.01], t(850) = -6.10, p < .001; Std. beta = -0.66, 95% CI [-0.87, -0.45])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -2.96, 95% CI [-3.93, -1.99], t(850) = -5.99, p < .001; Std. beta = -0.66, 95% CI [-0.88, -0.44])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

who

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict who with group and time_point (formula: who ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.61) and the part related to the fixed effects alone (marginal R2) is of 0.05. The model’s intercept, corresponding to group = control and time_point = 1st, is at 9.82 (95% CI [9.22, 10.42], t(850) = 32.18, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.16, 95% CI [-0.68, 1.01], t(850) = 0.38, p = 0.707; Std. beta = 0.04, 95% CI [-0.16, 0.24])
  • The effect of time point [2nd] is statistically significant and positive (beta = 0.73, 95% CI [0.14, 1.31], t(850) = 2.44, p = 0.015; Std. beta = 0.17, 95% CI [0.03, 0.31])
  • The effect of time point [3rd] is statistically significant and positive (beta = 0.93, 95% CI [0.33, 1.53], t(850) = 3.04, p = 0.002; Std. beta = 0.22, 95% CI [0.08, 0.36])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and positive (beta = 1.40, 95% CI [0.51, 2.29], t(850) = 3.10, p = 0.002; Std. beta = 0.33, 95% CI [0.12, 0.54])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and positive (beta = 1.63, 95% CI [0.72, 2.53], t(850) = 3.53, p < .001; Std. beta = 0.39, 95% CI [0.17, 0.60])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

phq

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict phq with group and time_point (formula: phq ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.68) and the part related to the fixed effects alone (marginal R2) is of 0.04. The model’s intercept, corresponding to group = control and time_point = 1st, is at 8.21 (95% CI [7.47, 8.95], t(850) = 21.70, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.59, 95% CI [-0.46, 1.64], t(850) = 1.11, p = 0.268; Std. beta = 0.11, 95% CI [-0.09, 0.32])
  • The effect of time point [2nd] is statistically significant and negative (beta = -0.78, 95% CI [-1.43, -0.13], t(850) = -2.34, p = 0.019; Std. beta = -0.15, 95% CI [-0.28, -0.02])
  • The effect of time point [3rd] is statistically non-significant and negative (beta = -0.66, 95% CI [-1.33, 5.02e-03], t(850) = -1.95, p = 0.052; Std. beta = -0.13, 95% CI [-0.26, 9.66e-04])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -1.73, 95% CI [-2.73, -0.74], t(850) = -3.43, p < .001; Std. beta = -0.33, 95% CI [-0.53, -0.14])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -2.42, 95% CI [-3.43, -1.41], t(850) = -4.69, p < .001; Std. beta = -0.47, 95% CI [-0.66, -0.27])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

gad

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict gad with group and time_point (formula: gad ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.67) and the part related to the fixed effects alone (marginal R2) is of 0.04. The model’s intercept, corresponding to group = control and time_point = 1st, is at 7.54 (95% CI [6.79, 8.28], t(850) = 19.75, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.48, 95% CI [-0.58, 1.54], t(850) = 0.89, p = 0.373; Std. beta = 0.09, 95% CI [-0.11, 0.30])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.44, 95% CI [-1.11, 0.23], t(850) = -1.29, p = 0.197; Std. beta = -0.08, 95% CI [-0.21, 0.04])
  • The effect of time point [3rd] is statistically non-significant and negative (beta = -0.58, 95% CI [-1.27, 0.10], t(850) = -1.66, p = 0.097; Std. beta = -0.11, 95% CI [-0.24, 0.02])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -2.07, 95% CI [-3.09, -1.06], t(850) = -4.01, p < .001; Std. beta = -0.40, 95% CI [-0.60, -0.20])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -2.38, 95% CI [-3.42, -1.35], t(850) = -4.52, p < .001; Std. beta = -0.46, 95% CI [-0.66, -0.26])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

wsas

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict wsas with group and time_point (formula: wsas ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.64) and the part related to the fixed effects alone (marginal R2) is of 0.03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 16.77 (95% CI [15.30, 18.24], t(850) = 22.41, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.08, 95% CI [-2.16, 1.99], t(850) = -0.08, p = 0.937; Std. beta = -8.25e-03, 95% CI [-0.21, 0.20])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.82, 95% CI [-2.18, 0.54], t(850) = -1.18, p = 0.238; Std. beta = -0.08, 95% CI [-0.21, 0.05])
  • The effect of time point [3rd] is statistically non-significant and negative (beta = -0.13, 95% CI [-1.53, 1.26], t(850) = -0.18, p = 0.853; Std. beta = -0.01, 95% CI [-0.15, 0.12])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -2.95, 95% CI [-5.02, -0.89], t(850) = -2.80, p = 0.005; Std. beta = -0.29, 95% CI [-0.49, -0.09])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -4.90, 95% CI [-7.01, -2.80], t(850) = -4.57, p < .001; Std. beta = -0.48, 95% CI [-0.69, -0.28])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

shps_arousal

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict shps_arousal with group and time_point (formula: shps_arousal ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.56) and the part related to the fixed effects alone (marginal R2) is of 0.11. The model’s intercept, corresponding to group = control and time_point = 1st, is at 3.02 (95% CI [2.91, 3.13], t(850) = 54.49, p < .001). Within this model:

  • The effect of group [treatment] is statistically significant and positive (beta = 0.16, 95% CI [8.91e-03, 0.32], t(850) = 2.07, p = 0.038; Std. beta = 0.21, 95% CI [0.01, 0.40])
  • The effect of time point [2nd] is statistically significant and negative (beta = -0.20, 95% CI [-0.31, -0.08], t(850) = -3.30, p < .001; Std. beta = -0.25, 95% CI [-0.39, -0.10])
  • The effect of time point [3rd] is statistically significant and negative (beta = -0.22, 95% CI [-0.34, -0.10], t(850) = -3.64, p < .001; Std. beta = -0.28, 95% CI [-0.43, -0.13])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -0.48, 95% CI [-0.65, -0.30], t(850) = -5.34, p < .001; Std. beta = -0.60, 95% CI [-0.83, -0.38])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.56, 95% CI [-0.74, -0.38], t(850) = -6.18, p < .001; Std. beta = -0.71, 95% CI [-0.94, -0.49])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

shps_schedule

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict shps_schedule with group and time_point (formula: shps_schedule ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.67) and the part related to the fixed effects alone (marginal R2) is of 0.05. The model’s intercept, corresponding to group = control and time_point = 1st, is at 3.53 (95% CI [3.40, 3.66], t(850) = 53.14, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.04, 95% CI [-0.14, 0.23], t(850) = 0.44, p = 0.659; Std. beta = 0.05, 95% CI [-0.16, 0.25])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.10, 95% CI [-0.22, 0.02], t(850) = -1.68, p = 0.093; Std. beta = -0.11, 95% CI [-0.24, 0.02])
  • The effect of time point [3rd] is statistically significant and negative (beta = -0.14, 95% CI [-0.26, -0.02], t(850) = -2.21, p = 0.027; Std. beta = -0.15, 95% CI [-0.28, -0.02])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -0.34, 95% CI [-0.52, -0.17], t(850) = -3.79, p < .001; Std. beta = -0.38, 95% CI [-0.57, -0.18])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.42, 95% CI [-0.60, -0.24], t(850) = -4.55, p < .001; Std. beta = -0.46, 95% CI [-0.66, -0.26])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

shps_behavior

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict shps_behavior with group and time_point (formula: shps_behavior ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.58) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 1.99 (95% CI [1.89, 2.08], t(850) = 39.01, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.13, 95% CI [-8.82e-03, 0.27], t(850) = 1.84, p = 0.066; Std. beta = 0.19, 95% CI [-0.01, 0.40])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 0.02, 95% CI [-0.07, 0.12], t(850) = 0.48, p = 0.629; Std. beta = 0.04, 95% CI [-0.11, 0.18])
  • The effect of time point [3rd] is statistically non-significant and positive (beta = 9.29e-03, 95% CI [-0.09, 0.11], t(850) = 0.18, p = 0.858; Std. beta = 0.01, 95% CI [-0.13, 0.16])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -0.24, 95% CI [-0.39, -0.09], t(850) = -3.18, p = 0.001; Std. beta = -0.35, 95% CI [-0.57, -0.14])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.33, 95% CI [-0.49, -0.18], t(850) = -4.26, p < .001; Std. beta = -0.48, 95% CI [-0.71, -0.26])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

shps_environment

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict shps_environment with group and time_point (formula: shps_environment ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.59) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 2.33 (95% CI [2.21, 2.45], t(850) = 38.46, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.06, 95% CI [-0.23, 0.11], t(850) = -0.72, p = 0.469; Std. beta = -0.08, 95% CI [-0.28, 0.13])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.06, 95% CI [-0.18, 0.06], t(850) = -0.97, p = 0.330; Std. beta = -0.07, 95% CI [-0.22, 0.07])
  • The effect of time point [3rd] is statistically non-significant and negative (beta = -0.06, 95% CI [-0.18, 0.06], t(850) = -0.97, p = 0.333; Std. beta = -0.07, 95% CI [-0.22, 0.07])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -0.09, 95% CI [-0.26, 0.09], t(850) = -0.94, p = 0.346; Std. beta = -0.10, 95% CI [-0.32, 0.11])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.26, 95% CI [-0.44, -0.08], t(850) = -2.81, p = 0.005; Std. beta = -0.32, 95% CI [-0.54, -0.10])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

dbas_consequence

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict dbas_consequence with group and time_point (formula: dbas_consequence ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.62) and the part related to the fixed effects alone (marginal R2) is of 0.12. The model’s intercept, corresponding to group = control and time_point = 1st, is at 6.59 (95% CI [6.31, 6.86], t(850) = 46.92, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.05, 95% CI [-0.34, 0.44], t(850) = 0.27, p = 0.787; Std. beta = 0.03, 95% CI [-0.17, 0.22])
  • The effect of time point [2nd] is statistically significant and negative (beta = -0.34, 95% CI [-0.61, -0.06], t(850) = -2.39, p = 0.017; Std. beta = -0.17, 95% CI [-0.30, -0.03])
  • The effect of time point [3rd] is statistically significant and negative (beta = -0.67, 95% CI [-0.95, -0.39], t(850) = -4.64, p < .001; Std. beta = -0.33, 95% CI [-0.47, -0.19])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -1.11, 95% CI [-1.53, -0.69], t(850) = -5.21, p < .001; Std. beta = -0.55, 95% CI [-0.76, -0.34])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -1.30, 95% CI [-1.72, -0.87], t(850) = -5.98, p < .001; Std. beta = -0.65, 95% CI [-0.86, -0.43])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

dbas_worry

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict dbas_worry with group and time_point (formula: dbas_worry ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.53) and the part related to the fixed effects alone (marginal R2) is of 0.16. The model’s intercept, corresponding to group = control and time_point = 1st, is at 14.20 (95% CI [13.65, 14.76], t(850) = 50.09, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.34, 95% CI [-0.45, 1.13], t(850) = 0.85, p = 0.395; Std. beta = 0.08, 95% CI [-0.11, 0.27])
  • The effect of time point [2nd] is statistically significant and negative (beta = -1.23, 95% CI [-1.86, -0.60], t(850) = -3.81, p < .001; Std. beta = -0.30, 95% CI [-0.45, -0.14])
  • The effect of time point [3rd] is statistically significant and negative (beta = -1.83, 95% CI [-2.48, -1.18], t(850) = -5.54, p < .001; Std. beta = -0.44, 95% CI [-0.60, -0.29])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -2.71, 95% CI [-3.67, -1.76], t(850) = -5.58, p < .001; Std. beta = -0.65, 95% CI [-0.88, -0.42])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -2.88, 95% CI [-3.85, -1.91], t(850) = -5.81, p < .001; Std. beta = -0.69, 95% CI [-0.93, -0.46])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

dbas_expectation

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict dbas_expectation with group and time_point (formula: dbas_expectation ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.60) and the part related to the fixed effects alone (marginal R2) is of 0.11. The model’s intercept, corresponding to group = control and time_point = 1st, is at 7.17 (95% CI [6.84, 7.51], t(850) = 41.60, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.28, 95% CI [-0.76, 0.19], t(850) = -1.17, p = 0.243; Std. beta = -0.12, 95% CI [-0.31, 0.08])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.34, 95% CI [-0.69, 1.34e-03], t(850) = -1.95, p = 0.051; Std. beta = -0.14, 95% CI [-0.28, 5.48e-04])
  • The effect of time point [3rd] is statistically significant and negative (beta = -0.78, 95% CI [-1.13, -0.42], t(850) = -4.31, p < .001; Std. beta = -0.32, 95% CI [-0.46, -0.17])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -1.25, 95% CI [-1.77, -0.73], t(850) = -4.70, p < .001; Std. beta = -0.51, 95% CI [-0.72, -0.30])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -1.28, 95% CI [-1.81, -0.75], t(850) = -4.72, p < .001; Std. beta = -0.52, 95% CI [-0.74, -0.31])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

dbas_medication

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict dbas_medication with group and time_point (formula: dbas_medication ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.56) and the part related to the fixed effects alone (marginal R2) is of 0.01. The model’s intercept, corresponding to group = control and time_point = 1st, is at 3.15 (95% CI [2.83, 3.46], t(850) = 19.55, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.09, 95% CI [-0.36, 0.54], t(850) = 0.39, p = 0.695; Std. beta = 0.04, 95% CI [-0.17, 0.25])
  • The effect of time point [2nd] is statistically significant and positive (beta = 0.37, 95% CI [0.04, 0.69], t(850) = 2.23, p = 0.026; Std. beta = 0.17, 95% CI [0.02, 0.32])
  • The effect of time point [3rd] is statistically non-significant and positive (beta = 0.31, 95% CI [-0.02, 0.64], t(850) = 1.83, p = 0.067; Std. beta = 0.14, 95% CI [-0.01, 0.30])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -0.66, 95% CI [-1.15, -0.18], t(850) = -2.67, p = 0.008; Std. beta = -0.31, 95% CI [-0.53, -0.08])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.86, 95% CI [-1.35, -0.36], t(850) = -3.39, p < .001; Std. beta = -0.40, 95% CI [-0.63, -0.17])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

psas_somatic

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict psas_somatic with group and time_point (formula: psas_somatic ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.63) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 1.86 (95% CI [1.76, 1.96], t(850) = 36.76, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.04, 95% CI [-0.10, 0.19], t(850) = 0.62, p = 0.533; Std. beta = 0.07, 95% CI [-0.14, 0.27])
  • The effect of time point [2nd] is statistically significant and positive (beta = 0.14, 95% CI [0.05, 0.24], t(850) = 3.03, p = 0.002; Std. beta = 0.21, 95% CI [0.07, 0.35])
  • The effect of time point [3rd] is statistically non-significant and positive (beta = 8.14e-03, 95% CI [-0.09, 0.10], t(850) = 0.17, p = 0.867; Std. beta = 0.01, 95% CI [-0.13, 0.15])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -0.31, 95% CI [-0.45, -0.17], t(850) = -4.27, p < .001; Std. beta = -0.45, 95% CI [-0.65, -0.24])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.24, 95% CI [-0.38, -0.10], t(850) = -3.28, p = 0.001; Std. beta = -0.35, 95% CI [-0.56, -0.14])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

psas_cognitive

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict psas_cognitive with group and time_point (formula: psas_cognitive ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.60) and the part related to the fixed effects alone (marginal R2) is of 0.09. The model’s intercept, corresponding to group = control and time_point = 1st, is at 2.87 (95% CI [2.75, 3.00], t(850) = 45.28, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.10, 95% CI [-0.08, 0.27], t(850) = 1.11, p = 0.269; Std. beta = 0.11, 95% CI [-0.09, 0.31])
  • The effect of time point [2nd] is statistically significant and negative (beta = -0.20, 95% CI [-0.33, -0.08], t(850) = -3.20, p = 0.001; Std. beta = -0.23, 95% CI [-0.37, -0.09])
  • The effect of time point [3rd] is statistically significant and negative (beta = -0.36, 95% CI [-0.48, -0.23], t(850) = -5.44, p < .001; Std. beta = -0.40, 95% CI [-0.54, -0.26])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -0.43, 95% CI [-0.62, -0.24], t(850) = -4.49, p < .001; Std. beta = -0.49, 95% CI [-0.70, -0.28])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.41, 95% CI [-0.61, -0.22], t(850) = -4.21, p < .001; Std. beta = -0.47, 95% CI [-0.68, -0.25])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

psqi_global

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict psqi_global with group and time_point (formula: psqi_global ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.56) and the part related to the fixed effects alone (marginal R2) is of 0.15. The model’s intercept, corresponding to group = control and time_point = 1st, is at 10.72 (95% CI [10.26, 11.18], t(850) = 45.26, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.29, 95% CI [-0.37, 0.95], t(850) = 0.87, p = 0.386; Std. beta = 0.08, 95% CI [-0.11, 0.27])
  • The effect of time point [2nd] is statistically significant and negative (beta = -1.31, 95% CI [-1.82, -0.81], t(850) = -5.10, p < .001; Std. beta = -0.38, 95% CI [-0.53, -0.23])
  • The effect of time point [3rd] is statistically significant and negative (beta = -1.32, 95% CI [-1.84, -0.80], t(850) = -4.99, p < .001; Std. beta = -0.38, 95% CI [-0.53, -0.23])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -1.86, 95% CI [-2.62, -1.10], t(850) = -4.79, p < .001; Std. beta = -0.54, 95% CI [-0.76, -0.32])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -2.44, 95% CI [-3.22, -1.66], t(850) = -6.16, p < .001; Std. beta = -0.71, 95% CI [-0.93, -0.48])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

mic_attention

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict mic_attention with group and time_point (formula: mic_attention ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.60) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 1.30 (95% CI [1.19, 1.41], t(850) = 22.91, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.12, 95% CI [-0.04, 0.28], t(850) = 1.52, p = 0.129; Std. beta = 0.16, 95% CI [-0.05, 0.36])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.02, 95% CI [-0.13, 0.09], t(850) = -0.39, p = 0.693; Std. beta = -0.03, 95% CI [-0.17, 0.11])
  • The effect of time point [3rd] is statistically non-significant and positive (beta = 0.03, 95% CI [-0.08, 0.14], t(850) = 0.50, p = 0.614; Std. beta = 0.04, 95% CI [-0.11, 0.18])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -0.25, 95% CI [-0.41, -0.08], t(850) = -2.95, p = 0.003; Std. beta = -0.32, 95% CI [-0.54, -0.11])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.38, 95% CI [-0.55, -0.21], t(850) = -4.47, p < .001; Std. beta = -0.50, 95% CI [-0.72, -0.28])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

mic_executive

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict mic_executive with group and time_point (formula: mic_executive ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.63) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 1.28 (95% CI [1.17, 1.39], t(850) = 22.00, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.07, 95% CI [-0.09, 0.23], t(850) = 0.82, p = 0.415; Std. beta = 0.08, 95% CI [-0.12, 0.29])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.03, 95% CI [-0.14, 0.07], t(850) = -0.62, p = 0.537; Std. beta = -0.04, 95% CI [-0.18, 0.09])
  • The effect of time point [3rd] is statistically non-significant and negative (beta = -0.05, 95% CI [-0.16, 0.06], t(850) = -0.85, p = 0.394; Std. beta = -0.06, 95% CI [-0.20, 0.08])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -0.16, 95% CI [-0.32, 2.33e-03], t(850) = -1.93, p = 0.053; Std. beta = -0.20, 95% CI [-0.41, 2.95e-03])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.27, 95% CI [-0.44, -0.11], t(850) = -3.25, p = 0.001; Std. beta = -0.35, 95% CI [-0.55, -0.14])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

mic_memory

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict mic_memory with group and time_point (formula: mic_memory ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.67) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 1.33 (95% CI [1.22, 1.44], t(850) = 23.30, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.07, 95% CI [-0.09, 0.22], t(850) = 0.81, p = 0.417; Std. beta = 0.08, 95% CI [-0.12, 0.29])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 0.03, 95% CI [-0.07, 0.13], t(850) = 0.62, p = 0.537; Std. beta = 0.04, 95% CI [-0.09, 0.17])
  • The effect of time point [3rd] is statistically non-significant and negative (beta = -0.06, 95% CI [-0.16, 0.04], t(850) = -1.14, p = 0.253; Std. beta = -0.08, 95% CI [-0.21, 0.06])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -0.28, 95% CI [-0.43, -0.12], t(850) = -3.56, p < .001; Std. beta = -0.36, 95% CI [-0.55, -0.16])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.22, 95% CI [-0.38, -0.07], t(850) = -2.83, p = 0.005; Std. beta = -0.29, 95% CI [-0.49, -0.09])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

nb_pcs

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict nb_pcs with group and time_point (formula: nb_pcs ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.66) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 46.33 (95% CI [45.04, 47.63], t(850) = 70.36, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.14, 95% CI [-1.96, 1.69], t(850) = -0.15, p = 0.881; Std. beta = -0.02, 95% CI [-0.22, 0.19])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.87, 95% CI [-2.03, 0.28], t(850) = -1.48, p = 0.140; Std. beta = -0.10, 95% CI [-0.23, 0.03])
  • The effect of time point [3rd] is statistically non-significant and negative (beta = -0.75, 95% CI [-1.94, 0.43], t(850) = -1.24, p = 0.213; Std. beta = -0.08, 95% CI [-0.22, 0.05])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and positive (beta = 2.76, 95% CI [1.00, 4.51], t(850) = 3.08, p = 0.002; Std. beta = 0.31, 95% CI [0.11, 0.51])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and positive (beta = 3.17, 95% CI [1.38, 4.96], t(850) = 3.47, p < .001; Std. beta = 0.35, 95% CI [0.15, 0.56])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

nb_mcs

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict nb_mcs with group and time_point (formula: nb_mcs ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.63) and the part related to the fixed effects alone (marginal R2) is of 0.06. The model’s intercept, corresponding to group = control and time_point = 1st, is at 39.90 (95% CI [38.39, 41.41], t(850) = 51.78, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.09, 95% CI [-2.05, 2.22], t(850) = 0.08, p = 0.938; Std. beta = 7.98e-03, 95% CI [-0.19, 0.21])
  • The effect of time point [2nd] is statistically significant and positive (beta = 2.00, 95% CI [0.55, 3.45], t(850) = 2.71, p = 0.007; Std. beta = 0.19, 95% CI [0.05, 0.32])
  • The effect of time point [3rd] is statistically significant and positive (beta = 2.30, 95% CI [0.81, 3.78], t(850) = 3.03, p = 0.002; Std. beta = 0.22, 95% CI [0.08, 0.35])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and positive (beta = 3.57, 95% CI [1.37, 5.77], t(850) = 3.19, p = 0.001; Std. beta = 0.33, 95% CI [0.13, 0.54])
  • The interaction effect of time point [3rd] on group [treatment] is statistically significant and positive (beta = 4.63, 95% CI [2.40, 6.87], t(850) = 4.06, p < .001; Std. beta = 0.43, 95% CI [0.22, 0.64])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

Likelihood ratio tests

outcome

model

npar

AIC

BIC

logLik

deviance

Chisq

Df

p

isi

null

3

4,944.027

4,958.291

-2,469.013

4,938.027

isi

random

8

4,610.891

4,648.927

-2,297.445

4,594.891

343.136

5

0.000

who

null

3

4,675.313

4,689.577

-2,334.657

4,669.313

who

random

8

4,609.710

4,647.747

-2,296.855

4,593.710

75.603

5

0.000

phq

null

3

4,953.672

4,967.936

-2,473.836

4,947.672

phq

random

8

4,887.168

4,925.204

-2,435.584

4,871.168

76.505

5

0.000

gad

null

3

4,971.723

4,985.987

-2,482.862

4,965.723

gad

random

8

4,913.850

4,951.886

-2,448.925

4,897.850

67.874

5

0.000

wsas

null

3

6,140.885

6,155.148

-3,067.442

6,134.885

wsas

random

8

6,103.487

6,141.524

-3,043.744

6,087.487

47.397

5

0.000

shps_arousal

null

3

1,905.371

1,919.635

-949.686

1,899.371

shps_arousal

random

8

1,753.916

1,791.953

-868.958

1,737.916

161.455

5

0.000

shps_schedule

null

3

1,991.252

2,005.516

-992.626

1,985.252

shps_schedule

random

8

1,923.633

1,961.670

-953.817

1,907.633

77.619

5

0.000

shps_behavior

null

3

1,571.659

1,585.923

-782.829

1,565.659

shps_behavior

random

8

1,548.993

1,587.030

-766.497

1,532.993

32.665

5

0.000

shps_environment

null

3

1,858.909

1,873.173

-926.454

1,852.909

shps_environment

random

8

1,843.481

1,881.518

-913.740

1,827.481

25.428

5

0.000

dbas_consequence

null

3

3,456.690

3,470.953

-1,725.345

3,450.690

dbas_consequence

random

8

3,296.900

3,334.937

-1,640.450

3,280.900

169.790

5

0.000

dbas_worry

null

3

4,797.505

4,811.769

-2,395.753

4,791.505

dbas_worry

random

8

4,603.324

4,641.361

-2,293.662

4,587.324

204.181

5

0.000

dbas_expectation

null

3

3,790.765

3,805.029

-1,892.382

3,784.765

dbas_expectation

random

8

3,663.068

3,701.105

-1,823.534

3,647.068

137.697

5

0.000

dbas_medication

null

3

3,552.532

3,566.796

-1,773.266

3,546.532

dbas_medication

random

8

3,546.012

3,584.049

-1,765.006

3,530.012

16.520

5

0.006

psas_somatic

null

3

1,509.946

1,524.210

-751.973

1,503.946

psas_somatic

random

8

1,487.950

1,525.987

-735.975

1,471.950

31.996

5

0.000

psas_cognitive

null

3

2,070.394

2,084.658

-1,032.197

2,064.394

psas_cognitive

random

8

1,936.726

1,974.763

-960.363

1,920.726

143.668

5

0.000

psqi_global

null

3

4,454.534

4,468.798

-2,224.267

4,448.534

psqi_global

random

8

4,260.007

4,298.044

-2,122.003

4,244.007

204.527

5

0.000

mic_attention

null

3

1,743.532

1,757.796

-868.766

1,737.532

mic_attention

random

8

1,718.887

1,756.924

-851.443

1,702.887

34.646

5

0.000

mic_executive

null

3

1,743.054

1,757.318

-868.527

1,737.054

mic_executive

random

8

1,725.760

1,763.797

-854.880

1,709.760

27.294

5

0.000

mic_memory

null

3

1,677.277

1,691.541

-835.639

1,671.277

mic_memory

random

8

1,656.513

1,694.550

-820.257

1,640.513

30.764

5

0.000

nb_pcs

null

3

5,861.904

5,876.168

-2,927.952

5,855.904

nb_pcs

random

8

5,852.914

5,890.951

-2,918.457

5,836.914

18.990

5

0.002

nb_mcs

null

3

6,257.285

6,271.548

-3,125.642

6,251.285

nb_mcs

random

8

6,181.821

6,219.858

-3,082.911

6,165.821

85.463

5

0.000

Post hoc analysis

Table

outcome

time

control

treatment

between

n

estimate

within es

n

estimate

within es

p

es

isi

1st

179

13.53 ± 3.83

179

13.40 ± 3.83

0.751

0.045

isi

2nd

148

11.07 ± 3.76

0.864

109

7.98 ± 3.67

1.907

0.000

1.088

isi

3rd

138

10.66 ± 3.73

1.010

105

7.57 ± 3.66

2.053

0.000

1.088

who

1st

179

9.82 ± 4.08

179

9.98 ± 4.08

0.708

-0.062

who

2nd

148

10.55 ± 3.96

-0.279

109

12.11 ± 3.80

-0.814

0.001

-0.598

who

3rd

138

10.75 ± 3.91

-0.355

105

12.54 ± 3.77

-0.978

0.000

-0.684

phq

1st

179

8.21 ± 5.06

179

8.80 ± 5.06

0.269

-0.204

phq

2nd

148

7.43 ± 4.86

0.268

109

6.29 ± 4.60

0.864

0.055

0.393

phq

3rd

138

7.55 ± 4.78

0.229

105

5.72 ± 4.56

1.061

0.003

0.628

gad

1st

179

7.54 ± 5.10

179

8.02 ± 5.10

0.374

-0.162

gad

2nd

148

7.10 ± 4.91

0.148

109

5.50 ± 4.65

0.844

0.008

0.535

gad

3rd

138

6.96 ± 4.83

0.195

105

5.05 ± 4.61

0.996

0.002

0.639

wsas

1st

179

16.77 ± 10.01

179

16.69 ± 10.01

0.937

0.014

wsas

2nd

148

15.95 ± 9.66

0.135

109

12.92 ± 9.20

0.621

0.011

0.500

wsas

3rd

138

16.64 ± 9.51

0.022

105

11.65 ± 9.13

0.829

0.000

0.822

shps_arousal

1st

179

3.02 ± 0.74

179

3.18 ± 0.74

0.039

-0.313

shps_arousal

2nd

148

2.83 ± 0.73

0.376

109

2.51 ± 0.70

1.293

0.000

0.605

shps_arousal

3rd

138

2.80 ± 0.72

0.425

105

2.40 ± 0.70

1.507

0.000

0.769

shps_schedule

1st

179

3.53 ± 0.89

179

3.58 ± 0.89

0.659

-0.079

shps_schedule

2nd

148

3.43 ± 0.86

0.192

109

3.13 ± 0.81

0.852

0.004

0.580

shps_schedule

3rd

138

3.40 ± 0.84

0.259

105

3.02 ± 0.81

1.065

0.000

0.726

shps_behavior

1st

179

1.99 ± 0.68

179

2.12 ± 0.68

0.067

-0.298

shps_behavior

2nd

148

2.01 ± 0.66

-0.055

109

1.90 ± 0.64

0.495

0.172

0.252

shps_behavior

3rd

138

1.99 ± 0.65

-0.021

105

1.79 ± 0.63

0.729

0.016

0.452

shps_environment

1st

179

2.33 ± 0.81

179

2.27 ± 0.81

0.469

0.119

shps_environment

2nd

148

2.27 ± 0.79

0.111

109

2.13 ± 0.76

0.274

0.129

0.281

shps_environment

3rd

138

2.27 ± 0.78

0.113

105

1.95 ± 0.75

0.608

0.001

0.613

dbas_consequence

1st

179

6.59 ± 1.88

179

6.64 ± 1.88

0.787

-0.043

dbas_consequence

2nd

148

6.25 ± 1.83

0.273

109

5.19 ± 1.76

1.173

0.000

0.857

dbas_consequence

3rd

138

5.92 ± 1.80

0.543

105

4.67 ± 1.75

1.596

0.000

1.009

dbas_worry

1st

179

14.20 ± 3.79

179

14.54 ± 3.79

0.396

-0.120

dbas_worry

2nd

148

12.97 ± 3.73

0.433

109

10.60 ± 3.65

1.385

0.000

0.833

dbas_worry

3rd

138

12.37 ± 3.70

0.644

105

9.83 ± 3.64

1.655

0.000

0.892

dbas_expectation

1st

179

7.17 ± 2.31

179

6.89 ± 2.31

0.243

0.185

dbas_expectation

2nd

148

6.83 ± 2.25

0.223

109

5.30 ± 2.17

1.033

0.000

0.995

dbas_expectation

3rd

138

6.40 ± 2.22

0.504

105

4.83 ± 2.15

1.334

0.000

1.015

dbas_medication

1st

179

3.15 ± 2.15

179

3.24 ± 2.15

0.695

-0.062

dbas_medication

2nd

148

3.51 ± 2.10

-0.254

109

2.94 ± 2.02

0.206

0.027

0.399

dbas_medication

3rd

138

3.45 ± 2.07

-0.214

105

2.69 ± 2.01

0.382

0.004

0.534

psas_somatic

1st

179

1.86 ± 0.68

179

1.91 ± 0.68

0.533

-0.108

psas_somatic

2nd

148

2.00 ± 0.65

-0.347

109

1.74 ± 0.62

0.393

0.001

0.632

psas_somatic

3rd

138

1.87 ± 0.64

-0.020

105

1.67 ± 0.62

0.561

0.017

0.472

psas_cognitive

1st

179

2.87 ± 0.85

179

2.97 ± 0.85

0.269

-0.177

psas_cognitive

2nd

148

2.67 ± 0.82

0.365

109

2.33 ± 0.79

1.141

0.001

0.598

psas_cognitive

3rd

138

2.52 ± 0.81

0.636

105

2.20 ± 0.79

1.376

0.002

0.563

psqi_global

1st

179

10.72 ± 3.17

179

11.01 ± 3.17

0.386

-0.128

psqi_global

2nd

148

9.41 ± 3.10

0.579

109

7.84 ± 3.02

1.400

0.000

0.693

psqi_global

3rd

138

9.40 ± 3.07

0.582

105

7.25 ± 3.01

1.659

0.000

0.949

mic_attention

1st

179

1.30 ± 0.76

179

1.42 ± 0.76

0.130

-0.250

mic_attention

2nd

148

1.28 ± 0.73

0.045

109

1.15 ± 0.70

0.555

0.164

0.260

mic_attention

3rd

138

1.33 ± 0.72

-0.059

105

1.07 ± 0.70

0.729

0.005

0.537

mic_executive

1st

179

1.28 ± 0.78

179

1.35 ± 0.78

0.415

-0.141

mic_executive

2nd

148

1.25 ± 0.75

0.071

109

1.15 ± 0.72

0.406

0.318

0.194

mic_executive

3rd

138

1.23 ± 0.74

0.100

105

1.03 ± 0.71

0.674

0.028

0.433

mic_memory

1st

179

1.33 ± 0.76

179

1.40 ± 0.76

0.417

-0.147

mic_memory

2nd

148

1.36 ± 0.74

-0.071

109

1.15 ± 0.70

0.548

0.020

0.472

mic_memory

3rd

138

1.27 ± 0.72

0.134

105

1.12 ± 0.69

0.635

0.085

0.354

nb_pcs

1st

179

46.33 ± 8.81

179

46.20 ± 8.81

0.882

0.027

nb_pcs

2nd

148

45.46 ± 8.47

0.169

109

48.08 ± 8.03

-0.366

0.012

-0.509

nb_pcs

3rd

138

45.58 ± 8.34

0.146

105

48.61 ± 7.97

-0.469

0.004

-0.589

nb_mcs

1st

179

39.90 ± 10.31

179

39.98 ± 10.31

0.938

-0.013

nb_mcs

2nd

148

41.90 ± 9.98

-0.310

109

45.56 ± 9.54

-0.862

0.003

-0.565

nb_mcs

3rd

138

42.19 ± 9.83

-0.355

105

46.91 ± 9.48

-1.072

0.000

-0.730

Between group

isi

1st

t(640.33) = -0.32, p = 0.751, Cohen d = 0.05, 95% CI (-0.92 to 0.67)

2st

t(756.53) = -6.60, p = 0.000, Cohen d = 1.09, 95% CI (-4.01 to -2.17)

3rd

t(772.73) = -6.47, p = 0.000, Cohen d = 1.09, 95% CI (-4.03 to -2.15)

who

1st

t(548.35) = 0.38, p = 0.708, Cohen d = -0.06, 95% CI (-0.69 to 1.01)

2st

t(687.30) = 3.20, p = 0.001, Cohen d = -0.60, 95% CI (0.61 to 2.52)

3rd

t(706.86) = 3.61, p = 0.000, Cohen d = -0.68, 95% CI (0.82 to 2.76)

phq

1st

t(501.27) = 1.11, p = 0.269, Cohen d = -0.20, 95% CI (-0.46 to 1.64)

2st

t(637.01) = -1.92, p = 0.055, Cohen d = 0.39, 95% CI (-2.31 to 0.03)

3rd

t(656.35) = -3.03, p = 0.003, Cohen d = 0.63, 95% CI (-3.01 to -0.64)

gad

1st

t(506.73) = 0.89, p = 0.374, Cohen d = -0.16, 95% CI (-0.58 to 1.54)

2st

t(643.55) = -2.65, p = 0.008, Cohen d = 0.54, 95% CI (-2.77 to -0.41)

3rd

t(663.02) = -3.12, p = 0.002, Cohen d = 0.64, 95% CI (-3.10 to -0.71)

wsas

1st

t(522.73) = -0.08, p = 0.937, Cohen d = 0.01, 95% CI (-2.16 to 2.00)

2st

t(661.58) = -2.56, p = 0.011, Cohen d = 0.50, 95% CI (-5.37 to -0.71)

3rd

t(681.25) = -4.14, p = 0.000, Cohen d = 0.82, 95% CI (-7.35 to -2.62)

shps_arousal

1st

t(599.92) = 2.07, p = 0.039, Cohen d = -0.31, 95% CI (0.01 to 0.32)

2st

t(729.75) = -3.50, p = 0.000, Cohen d = 0.60, 95% CI (-0.49 to -0.14)

3rd

t(747.86) = -4.37, p = 0.000, Cohen d = 0.77, 95% CI (-0.58 to -0.22)

shps_schedule

1st

t(510.17) = 0.44, p = 0.659, Cohen d = -0.08, 95% CI (-0.14 to 0.23)

2st

t(647.57) = -2.89, p = 0.004, Cohen d = 0.58, 95% CI (-0.51 to -0.10)

3rd

t(667.10) = -3.57, p = 0.000, Cohen d = 0.73, 95% CI (-0.59 to -0.17)

shps_behavior

1st

t(556.99) = 1.84, p = 0.067, Cohen d = -0.30, 95% CI (-0.01 to 0.27)

2st

t(695.20) = -1.37, p = 0.172, Cohen d = 0.25, 95% CI (-0.27 to 0.05)

3rd

t(714.63) = -2.42, p = 0.016, Cohen d = 0.45, 95% CI (-0.36 to -0.04)

shps_environment

1st

t(552.74) = -0.72, p = 0.469, Cohen d = 0.12, 95% CI (-0.23 to 0.11)

2st

t(691.36) = -1.52, p = 0.129, Cohen d = 0.28, 95% CI (-0.34 to 0.04)

3rd

t(710.85) = -3.26, p = 0.001, Cohen d = 0.61, 95% CI (-0.52 to -0.13)

dbas_consequence

1st

t(561.16) = 0.27, p = 0.787, Cohen d = -0.04, 95% CI (-0.34 to 0.44)

2st

t(698.89) = -4.69, p = 0.000, Cohen d = 0.86, 95% CI (-1.50 to -0.61)

3rd

t(718.23) = -5.43, p = 0.000, Cohen d = 1.01, 95% CI (-1.69 to -0.79)

dbas_worry

1st

t(647.95) = 0.85, p = 0.396, Cohen d = -0.12, 95% CI (-0.45 to 1.13)

2st

t(761.09) = -5.10, p = 0.000, Cohen d = 0.83, 95% CI (-3.28 to -1.46)

3rd

t(776.86) = -5.35, p = 0.000, Cohen d = 0.89, 95% CI (-3.47 to -1.61)

dbas_expectation

1st

t(570.45) = -1.17, p = 0.243, Cohen d = 0.18, 95% CI (-0.76 to 0.19)

2st

t(706.84) = -5.52, p = 0.000, Cohen d = 1.00, 95% CI (-2.08 to -0.99)

3rd

t(725.95) = -5.54, p = 0.000, Cohen d = 1.02, 95% CI (-2.12 to -1.01)

dbas_medication

1st

t(571.35) = 0.39, p = 0.695, Cohen d = -0.06, 95% CI (-0.36 to 0.54)

2st

t(707.59) = -2.22, p = 0.027, Cohen d = 0.40, 95% CI (-1.08 to -0.07)

3rd

t(726.67) = -2.92, p = 0.004, Cohen d = 0.53, 95% CI (-1.29 to -0.25)

psas_somatic

1st

t(526.00) = 0.62, p = 0.533, Cohen d = -0.11, 95% CI (-0.10 to 0.19)

2st

t(665.07) = -3.25, p = 0.001, Cohen d = 0.63, 95% CI (-0.42 to -0.10)

3rd

t(684.76) = -2.40, p = 0.017, Cohen d = 0.47, 95% CI (-0.36 to -0.04)

psas_cognitive

1st

t(563.36) = 1.11, p = 0.269, Cohen d = -0.18, 95% CI (-0.08 to 0.28)

2st

t(700.80) = -3.29, p = 0.001, Cohen d = 0.60, 95% CI (-0.53 to -0.13)

3rd

t(720.09) = -3.04, p = 0.002, Cohen d = 0.56, 95% CI (-0.52 to -0.11)

psqi_global

1st

t(612.90) = 0.87, p = 0.386, Cohen d = -0.13, 95% CI (-0.37 to 0.95)

2st

t(738.87) = -4.07, p = 0.000, Cohen d = 0.69, 95% CI (-2.33 to -0.81)

3rd

t(756.43) = -5.47, p = 0.000, Cohen d = 0.95, 95% CI (-2.92 to -1.38)

mic_attention

1st

t(548.33) = 1.52, p = 0.130, Cohen d = -0.25, 95% CI (-0.04 to 0.28)

2st

t(687.28) = -1.39, p = 0.164, Cohen d = 0.26, 95% CI (-0.30 to 0.05)

3rd

t(706.84) = -2.83, p = 0.005, Cohen d = 0.54, 95% CI (-0.44 to -0.08)

mic_executive

1st

t(526.32) = 0.82, p = 0.415, Cohen d = -0.14, 95% CI (-0.09 to 0.23)

2st

t(665.41) = -1.00, p = 0.318, Cohen d = 0.19, 95% CI (-0.27 to 0.09)

3rd

t(685.10) = -2.20, p = 0.028, Cohen d = 0.43, 95% CI (-0.39 to -0.02)

mic_memory

1st

t(506.61) = 0.81, p = 0.417, Cohen d = -0.15, 95% CI (-0.09 to 0.22)

2st

t(643.41) = -2.33, p = 0.020, Cohen d = 0.47, 95% CI (-0.39 to -0.03)

3rd

t(662.88) = -1.73, p = 0.085, Cohen d = 0.35, 95% CI (-0.34 to 0.02)

nb_pcs

1st

t(507.78) = -0.15, p = 0.882, Cohen d = 0.03, 95% CI (-1.97 to 1.69)

2st

t(644.79) = 2.52, p = 0.012, Cohen d = -0.51, 95% CI (0.58 to 4.66)

3rd

t(664.28) = 2.88, p = 0.004, Cohen d = -0.59, 95% CI (0.96 to 5.10)

nb_mcs

1st

t(538.18) = 0.08, p = 0.938, Cohen d = -0.01, 95% CI (-2.06 to 2.23)

2st

t(677.52) = 2.98, p = 0.003, Cohen d = -0.57, 95% CI (1.24 to 6.07)

3rd

t(697.19) = 3.78, p = 0.000, Cohen d = -0.73, 95% CI (2.27 to 7.17)

Within treatment group

isi

1st vs 2st

t(596.66) = -14.90, p = 0.000, Cohen d = 1.91, 95% CI (-6.13 to -4.71)

1st vs 3rd

t(598.32) = -15.82, p = 0.000, Cohen d = 2.05, 95% CI (-6.56 to -5.11)

who

1st vs 2st

t(573.52) = 6.27, p = 0.000, Cohen d = -0.81, 95% CI (1.46 to 2.80)

1st vs 3rd

t(574.25) = 7.42, p = 0.000, Cohen d = -0.98, 95% CI (1.88 to 3.24)

phq

1st vs 2st

t(559.14) = -6.59, p = 0.000, Cohen d = 0.86, 95% CI (-3.26 to -1.76)

1st vs 3rd

t(559.52) = -7.98, p = 0.000, Cohen d = 1.06, 95% CI (-3.84 to -2.32)

gad

1st vs 2st

t(560.92) = -6.45, p = 0.000, Cohen d = 0.84, 95% CI (-3.28 to -1.75)

1st vs 3rd

t(561.33) = -7.50, p = 0.000, Cohen d = 1.00, 95% CI (-3.74 to -2.19)

wsas

1st vs 2st

t(565.95) = -4.76, p = 0.000, Cohen d = 0.62, 95% CI (-5.33 to -2.22)

1st vs 3rd

t(566.48) = -6.26, p = 0.000, Cohen d = 0.83, 95% CI (-6.61 to -3.46)

shps_arousal

1st vs 2st

t(587.16) = -10.04, p = 0.000, Cohen d = 1.29, 95% CI (-0.80 to -0.54)

1st vs 3rd

t(588.38) = -11.53, p = 0.000, Cohen d = 1.51, 95% CI (-0.92 to -0.65)

shps_schedule

1st vs 2st

t(562.02) = -6.51, p = 0.000, Cohen d = 0.85, 95% CI (-0.58 to -0.31)

1st vs 3rd

t(562.46) = -8.03, p = 0.000, Cohen d = 1.07, 95% CI (-0.69 to -0.42)

shps_behavior

1st vs 2st

t(575.94) = -3.81, p = 0.000, Cohen d = 0.49, 95% CI (-0.33 to -0.11)

1st vs 3rd

t(576.75) = -5.54, p = 0.000, Cohen d = 0.73, 95% CI (-0.44 to -0.21)

shps_environment

1st vs 2st

t(574.76) = -2.11, p = 0.071, Cohen d = 0.27, 95% CI (-0.28 to -0.01)

1st vs 3rd

t(575.53) = -4.62, p = 0.000, Cohen d = 0.61, 95% CI (-0.45 to -0.18)

dbas_consequence

1st vs 2st

t(577.09) = -9.04, p = 0.000, Cohen d = 1.17, 95% CI (-1.76 to -1.13)

1st vs 3rd

t(577.94) = -12.13, p = 0.000, Cohen d = 1.60, 95% CI (-2.29 to -1.65)

dbas_worry

1st vs 2st

t(598.35) = -10.84, p = 0.000, Cohen d = 1.39, 95% CI (-4.66 to -3.23)

1st vs 3rd

t(600.11) = -12.77, p = 0.000, Cohen d = 1.66, 95% CI (-5.44 to -3.99)

dbas_expectation

1st vs 2st

t(579.60) = -7.97, p = 0.000, Cohen d = 1.03, 95% CI (-1.98 to -1.20)

1st vs 3rd

t(580.53) = -10.16, p = 0.000, Cohen d = 1.33, 95% CI (-2.45 to -1.66)

dbas_medication

1st vs 2st

t(579.84) = -1.59, p = 0.223, Cohen d = 0.21, 95% CI (-0.66 to 0.07)

1st vs 3rd

t(580.78) = -2.91, p = 0.007, Cohen d = 0.38, 95% CI (-0.92 to -0.18)

psas_somatic

1st vs 2st

t(566.95) = -3.01, p = 0.005, Cohen d = 0.39, 95% CI (-0.27 to -0.06)

1st vs 3rd

t(567.51) = -4.24, p = 0.000, Cohen d = 0.56, 95% CI (-0.34 to -0.12)

psas_cognitive

1st vs 2st

t(577.69) = -8.80, p = 0.000, Cohen d = 1.14, 95% CI (-0.78 to -0.50)

1st vs 3rd

t(578.55) = -10.47, p = 0.000, Cohen d = 1.38, 95% CI (-0.91 to -0.63)

psqi_global

1st vs 2st

t(590.31) = -10.89, p = 0.000, Cohen d = 1.40, 95% CI (-3.75 to -2.60)

1st vs 3rd

t(591.67) = -12.72, p = 0.000, Cohen d = 1.66, 95% CI (-4.34 to -3.18)

mic_attention

1st vs 2st

t(573.51) = -4.27, p = 0.000, Cohen d = 0.56, 95% CI (-0.39 to -0.15)

1st vs 3rd

t(574.24) = -5.53, p = 0.000, Cohen d = 0.73, 95% CI (-0.48 to -0.23)

mic_executive

1st vs 2st

t(567.05) = -3.11, p = 0.004, Cohen d = 0.41, 95% CI (-0.31 to -0.07)

1st vs 3rd

t(567.61) = -5.09, p = 0.000, Cohen d = 0.67, 95% CI (-0.44 to -0.20)

mic_memory

1st vs 2st

t(560.88) = -4.19, p = 0.000, Cohen d = 0.55, 95% CI (-0.36 to -0.13)

1st vs 3rd

t(561.29) = -4.78, p = 0.000, Cohen d = 0.64, 95% CI (-0.40 to -0.17)

nb_pcs

1st vs 2st

t(561.26) = 2.80, p = 0.011, Cohen d = -0.37, 95% CI (0.56 to 3.21)

1st vs 3rd

t(561.68) = 3.54, p = 0.001, Cohen d = -0.47, 95% CI (1.07 to 3.76)

nb_mcs

1st vs 2st

t(570.59) = 6.62, p = 0.000, Cohen d = -0.86, 95% CI (3.92 to 7.23)

1st vs 3rd

t(571.24) = 8.12, p = 0.000, Cohen d = -1.07, 95% CI (5.25 to 8.61)

Within control group

isi

1st vs 2st

t(543.61) = -7.62, p = 0.000, Cohen d = 0.86, 95% CI (-3.09 to -1.82)

1st vs 3rd

t(548.86) = -8.69, p = 0.000, Cohen d = 1.01, 95% CI (-3.52 to -2.22)

who

1st vs 2st

t(531.57) = 2.44, p = 0.030, Cohen d = -0.28, 95% CI (0.14 to 1.32)

1st vs 3rd

t(534.82) = 3.03, p = 0.005, Cohen d = -0.36, 95% CI (0.33 to 1.53)

phq

1st vs 2st

t(524.61) = -2.34, p = 0.040, Cohen d = 0.27, 95% CI (-1.43 to -0.12)

1st vs 3rd

t(526.94) = -1.94, p = 0.105, Cohen d = 0.23, 95% CI (-1.34 to 0.01)

gad

1st vs 2st

t(525.45) = -1.29, p = 0.395, Cohen d = 0.15, 95% CI (-1.11 to 0.23)

1st vs 3rd

t(527.89) = -1.66, p = 0.195, Cohen d = 0.20, 95% CI (-1.27 to 0.11)

wsas

1st vs 2st

t(527.87) = -1.18, p = 0.478, Cohen d = 0.13, 95% CI (-2.18 to 0.55)

1st vs 3rd

t(530.62) = -0.18, p = 1.000, Cohen d = 0.02, 95% CI (-1.53 to 1.27)

shps_arousal

1st vs 2st

t(538.51) = -3.30, p = 0.002, Cohen d = 0.38, 95% CI (-0.31 to -0.08)

1st vs 3rd

t(542.85) = -3.64, p = 0.001, Cohen d = 0.42, 95% CI (-0.34 to -0.10)

shps_schedule

1st vs 2st

t(525.98) = -1.68, p = 0.187, Cohen d = 0.19, 95% CI (-0.22 to 0.02)

1st vs 3rd

t(528.49) = -2.21, p = 0.055, Cohen d = 0.26, 95% CI (-0.26 to -0.01)

shps_behavior

1st vs 2st

t(532.77) = 0.48, p = 1.000, Cohen d = -0.06, 95% CI (-0.07 to 0.12)

1st vs 3rd

t(536.21) = 0.18, p = 1.000, Cohen d = -0.02, 95% CI (-0.09 to 0.11)

shps_environment

1st vs 2st

t(532.18) = -0.97, p = 0.662, Cohen d = 0.11, 95% CI (-0.18 to 0.06)

1st vs 3rd

t(535.53) = -0.97, p = 0.666, Cohen d = 0.11, 95% CI (-0.18 to 0.06)

dbas_consequence

1st vs 2st

t(533.35) = -2.39, p = 0.034, Cohen d = 0.27, 95% CI (-0.61 to -0.06)

1st vs 3rd

t(536.87) = -4.64, p = 0.000, Cohen d = 0.54, 95% CI (-0.95 to -0.39)

dbas_worry

1st vs 2st

t(544.56) = -3.81, p = 0.000, Cohen d = 0.43, 95% CI (-1.87 to -0.60)

1st vs 3rd

t(549.98) = -5.54, p = 0.000, Cohen d = 0.64, 95% CI (-2.48 to -1.18)

dbas_expectation

1st vs 2st

t(534.61) = -1.95, p = 0.103, Cohen d = 0.22, 95% CI (-0.69 to 0.00)

1st vs 3rd

t(538.33) = -4.31, p = 0.000, Cohen d = 0.50, 95% CI (-1.13 to -0.42)

dbas_medication

1st vs 2st

t(534.74) = 2.23, p = 0.052, Cohen d = -0.25, 95% CI (0.04 to 0.69)

1st vs 3rd

t(538.47) = 1.83, p = 0.136, Cohen d = -0.21, 95% CI (-0.02 to 0.64)

psas_somatic

1st vs 2st

t(528.35) = 3.03, p = 0.005, Cohen d = -0.35, 95% CI (0.05 to 0.24)

1st vs 3rd

t(531.17) = 0.17, p = 1.000, Cohen d = -0.02, 95% CI (-0.09 to 0.10)

psas_cognitive

1st vs 2st

t(533.65) = -3.20, p = 0.003, Cohen d = 0.37, 95% CI (-0.33 to -0.08)

1st vs 3rd

t(537.21) = -5.44, p = 0.000, Cohen d = 0.64, 95% CI (-0.48 to -0.23)

psqi_global

1st vs 2st

t(540.17) = -5.10, p = 0.000, Cohen d = 0.58, 95% CI (-1.82 to -0.81)

1st vs 3rd

t(544.80) = -4.99, p = 0.000, Cohen d = 0.58, 95% CI (-1.84 to -0.80)

mic_attention

1st vs 2st

t(531.56) = -0.39, p = 1.000, Cohen d = 0.05, 95% CI (-0.13 to 0.09)

1st vs 3rd

t(534.82) = 0.50, p = 1.000, Cohen d = -0.06, 95% CI (-0.08 to 0.14)

mic_executive

1st vs 2st

t(528.40) = -0.62, p = 1.000, Cohen d = 0.07, 95% CI (-0.14 to 0.07)

1st vs 3rd

t(531.22) = -0.85, p = 0.789, Cohen d = 0.10, 95% CI (-0.16 to 0.06)

mic_memory

1st vs 2st

t(525.43) = 0.62, p = 1.000, Cohen d = -0.07, 95% CI (-0.07 to 0.13)

1st vs 3rd

t(527.87) = -1.14, p = 0.508, Cohen d = 0.13, 95% CI (-0.16 to 0.04)

nb_pcs

1st vs 2st

t(525.61) = -1.48, p = 0.280, Cohen d = 0.17, 95% CI (-2.03 to 0.29)

1st vs 3rd

t(528.08) = -1.24, p = 0.428, Cohen d = 0.15, 95% CI (-1.94 to 0.44)

nb_mcs

1st vs 2st

t(530.12) = 2.71, p = 0.014, Cohen d = -0.31, 95% CI (0.55 to 3.46)

1st vs 3rd

t(533.18) = 3.03, p = 0.005, Cohen d = -0.36, 95% CI (0.81 to 3.79)

Plot

Clinical significance

T1

T2

T3

outcome

control1

treatment1

p-value2

control1

treatment1

p-value2

control1

treatment1

p-value2

isi

89%

85%

0.206

61%

31%

0.000

56%

29%

0.000

psqi

96%

97%

0.586

89%

74%

0.003

89%

65%

0.000

phq

31%

38%

0.148

32%

19%

0.019

30%

18%

0.038

gad

30%

33%

0.494

26%

17%

0.061

27%

16%

0.049

wsas

74%

72%

0.721

68%

55%

0.041

69%

49%

0.001

1%

2Pearson's Chi-squared test